Abstract
The authors propose an additional level of parallelism, called
multi-associativity, as a framework for simultaneously performing
associative computation on data sets mapped to irregular, non-uniform,
aggregates of processing elements (PEs). They introduce algorithms
developed for the CAAPP to simulate efficiently within aggregates of PEs
simultaneously the associative algorithms typically supported in
hardware at the array level. Some of the results are: the efficient
application of existing associative algorithms to arbitrary aggregates
of PEs in parallel and the development of multi-associative algorithms,
among them parallel prefix and convex hull. The multi-associative
framework also extends the associative paradigm by allowing operation on
and among aggregates themselves, operations not defined when the entity
in question is always an entire array
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